{"cells":[{"cell_type":"code","source":["### Install the necessary Libraries before Importing them\n","### Supporting Tools Needed\n","### Generate a pandas profiling report to explore the dataset\n","### Generating a pandas profiling report helps in exploring the dataset by providing a comprehensive overview of its structure, statistics, distributions, missing values, correlations, and potential issues, enabling efficient data understanding and initial insights for further analysis and data preprocessing decisions.\n","!pip install pandas_profiling"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"39NTFMtUdPPe","executionInfo":{"status":"ok","timestamp":1723200581957,"user_tz":-180,"elapsed":3705,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}},"outputId":"8a82fa18-ac77-405b-80b5-1269b7523691","collapsed":true},"execution_count":136,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pandas_profiling in 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(e.g. pd.read_csv)\n","\n","# Visualization\n","# matplotlib.pyplot used for data visualization as it provides a variety of plotting functionalities.\n","# seaborn used for creating aesthetically pleasing and informative statistical graphics.\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline\n","\n"]},{"cell_type":"code","execution_count":138,"metadata":{"id":"46Ob8O2qcOOk","executionInfo":{"status":"ok","timestamp":1723200581957,"user_tz":-180,"elapsed":12,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["# Reading the data\n","student = pd.read_csv('students_dataset_article.csv')"]},{"cell_type":"code","execution_count":139,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1723200581957,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"huNpJv8_ch8q","outputId":"4eb2aeb8-4104-4187-d01d-8fad1cd55443"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(752, 38)"]},"metadata":{},"execution_count":139}],"source":["# Shape of data to see number of rows and columns\n","student.shape"]},{"cell_type":"code","execution_count":140,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":256},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1723200581957,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"6oU2ZixmckcR","outputId":"611f7767-e377-4cb5-924f-ab56fadadaba"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   id gender  age_join_uni ethnic_region_origin region_residence    religion  \\\n","0   1   Male            19            West Nile   Central Uganda  Protestant   \n","1   2   Male            30       Central Uganda   Central Uganda       Other   \n","2   3   Male            18       Western Uganda   Central Uganda  Protestant   \n","3   4   Male            19      Northern Uganda   Central Uganda  Protestant   \n","4   5   Male            20      Northern Uganda   Central Uganda  Protestant   \n","\n","  marital_status any_children  year_alevel region_alevel_school  ... clubs  \\\n","0         Single           No       2002.0       Central Uganda  ...     0   \n","1         Single           No       2006.0       Central Uganda  ...     0   \n","2         Single           No       2002.0       Central Uganda  ...     0   \n","3         Single           No       2004.0       Central Uganda  ...     0   \n","4         Single           No       2002.0       Central Uganda  ...     0   \n","\n","   ministry_religious None Other parent_alive parents_marital_status  \\\n","0                   0    0     0          Yes                 Single   \n","1                   0    1     0          Yes                Married   \n","2                   0    1     0          Yes                Widowed   \n","3                   1    0     0          Yes                Widowed   \n","4                   0    0     0          Yes                Married   \n","\n","   parents_employment_status other_children_schooling parents_academics  \\\n","0                   Employed                      Yes           Masters   \n","1                   Employed                      Yes       High School   \n","2                   Employed                      Yes         Bachelors   \n","3                   Employed                      Yes           Masters   \n","4                   Employed                      Yes         Bachelors   \n","\n","   cgpa_clean  \n","0        3.32  \n","1        4.40  \n","2        3.50  \n","3        3.89  \n","4        3.38  \n","\n","[5 rows x 38 columns]"],"text/html":["\n","  <div id=\"df-ff97caa2-bcf6-4bbe-8367-25fad616427d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>gender</th>\n","      <th>age_join_uni</th>\n","      <th>ethnic_region_origin</th>\n","      <th>region_residence</th>\n","      <th>religion</th>\n","      <th>marital_status</th>\n","      <th>any_children</th>\n","      <th>year_alevel</th>\n","      <th>region_alevel_school</th>\n","      <th>...</th>\n","      <th>clubs</th>\n","      <th>ministry_religious</th>\n","      <th>None</th>\n","      <th>Other</th>\n","      <th>parent_alive</th>\n","      <th>parents_marital_status</th>\n","      <th>parents_employment_status</th>\n","      <th>other_children_schooling</th>\n","      <th>parents_academics</th>\n","      <th>cgpa_clean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Male</td>\n","      <td>19</td>\n","      <td>West Nile</td>\n","      <td>Central Uganda</td>\n","      <td>Protestant</td>\n","      <td>Single</td>\n","      <td>No</td>\n","      <td>2002.0</td>\n","      <td>Central Uganda</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Single</td>\n","      <td>Employed</td>\n","      <td>Yes</td>\n","      <td>Masters</td>\n","      <td>3.32</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Male</td>\n","      <td>30</td>\n","      <td>Central Uganda</td>\n","      <td>Central Uganda</td>\n","      <td>Other</td>\n","      <td>Single</td>\n","      <td>No</td>\n","      <td>2006.0</td>\n","      <td>Central Uganda</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Married</td>\n","      <td>Employed</td>\n","      <td>Yes</td>\n","      <td>High School</td>\n","      <td>4.40</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>Male</td>\n","      <td>18</td>\n","      <td>Western Uganda</td>\n","      <td>Central Uganda</td>\n","      <td>Protestant</td>\n","      <td>Single</td>\n","      <td>No</td>\n","      <td>2002.0</td>\n","      <td>Central Uganda</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Widowed</td>\n","      <td>Employed</td>\n","      <td>Yes</td>\n","      <td>Bachelors</td>\n","      <td>3.50</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Male</td>\n","      <td>19</td>\n","      <td>Northern Uganda</td>\n","      <td>Central Uganda</td>\n","      <td>Protestant</td>\n","      <td>Single</td>\n","      <td>No</td>\n","      <td>2004.0</td>\n","      <td>Central Uganda</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Widowed</td>\n","      <td>Employed</td>\n","      <td>Yes</td>\n","      <td>Masters</td>\n","      <td>3.89</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Male</td>\n","      <td>20</td>\n","      <td>Northern Uganda</td>\n","      <td>Central Uganda</td>\n","      <td>Protestant</td>\n","      <td>Single</td>\n","      <td>No</td>\n","      <td>2002.0</td>\n","      <td>Central Uganda</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Yes</td>\n","      <td>Married</td>\n","      <td>Employed</td>\n","      <td>Yes</td>\n","      <td>Bachelors</td>\n","      <td>3.38</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 38 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff97caa2-bcf6-4bbe-8367-25fad616427d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-0d9ea4ce-26a5-46e8-a3b7-22bc63a5d1a4\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d9ea4ce-26a5-46e8-a3b7-22bc63a5d1a4')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-0d9ea4ce-26a5-46e8-a3b7-22bc63a5d1a4 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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2015.091153    4.672611  1985.0  2014.00  2017.00   \n","alevel_points       745.0    14.146309    3.533072     3.0    12.00    14.00   \n","program_duration    752.0     3.136968    0.407801     2.0     3.00     3.00   \n","year_entry_uni      752.0  2016.796543    4.015926  1984.0  2016.00  2018.00   \n","sports              752.0     0.303191    0.459943     0.0     0.00     0.00   \n","leadership          752.0     0.244681    0.430184     0.0     0.00     0.00   \n","volunteering        752.0     0.263298    0.440716     0.0     0.00     0.00   \n","clubs               752.0     0.168883    0.374898     0.0     0.00     0.00   \n","ministry_religious  752.0     0.244681    0.430184     0.0     0.00     0.00   \n","None                752.0     0.289894    0.454015     0.0     0.00     0.00   \n","Other               752.0     0.007979    0.089026     0.0     0.00     0.00   \n","cgpa_clean          752.0     3.764628    0.446955     2.3     3.46     3.78   \n","\n","                          75%      max  \n","id                   564.2500   752.00  \n","age_join_uni          21.0000    37.00  \n","year_alevel         2018.0000  2020.00  \n","alevel_points         17.0000    25.00  \n","program_duration       3.0000     5.00  \n","year_entry_uni      2019.0000  2021.00  \n","sports                 1.0000     1.00  \n","leadership             0.0000     1.00  \n","volunteering           1.0000     1.00  \n","clubs                  0.0000     1.00  \n","ministry_religious     0.0000     1.00  \n","None                   1.0000     1.00  \n","Other                  0.0000     1.00  \n","cgpa_clean             4.0825     4.78  "],"text/html":["\n","  <div id=\"df-ea136a88-4881-46ae-9b25-57701871064d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th 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<td>746.0</td>\n","      <td>2015.091153</td>\n","      <td>4.672611</td>\n","      <td>1985.0</td>\n","      <td>2014.00</td>\n","      <td>2017.00</td>\n","      <td>2018.0000</td>\n","      <td>2020.00</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_points</th>\n","      <td>745.0</td>\n","      <td>14.146309</td>\n","      <td>3.533072</td>\n","      <td>3.0</td>\n","      <td>12.00</td>\n","      <td>14.00</td>\n","      <td>17.0000</td>\n","      <td>25.00</td>\n","    </tr>\n","    <tr>\n","      <th>program_duration</th>\n","      <td>752.0</td>\n","      <td>3.136968</td>\n","      <td>0.407801</td>\n","      <td>2.0</td>\n","      <td>3.00</td>\n","      <td>3.00</td>\n","      <td>3.0000</td>\n","      <td>5.00</td>\n","    </tr>\n","    <tr>\n","      <th>year_entry_uni</th>\n","      <td>752.0</td>\n","      <td>2016.796543</td>\n","      <td>4.015926</td>\n","      <td>1984.0</td>\n","      <td>2016.00</td>\n","      <td>2018.00</td>\n","      <td>2019.0000</td>\n","      <td>2021.00</td>\n","    </tr>\n","    <tr>\n","      <th>sports</th>\n","      <td>752.0</td>\n","      <td>0.303191</td>\n","      <td>0.459943</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>leadership</th>\n","      <td>752.0</td>\n","      <td>0.244681</td>\n","      <td>0.430184</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>volunteering</th>\n","      <td>752.0</td>\n","      <td>0.263298</td>\n","      <td>0.440716</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>clubs</th>\n","      <td>752.0</td>\n","      <td>0.168883</td>\n","      <td>0.374898</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>ministry_religious</th>\n","      <td>752.0</td>\n","      <td>0.244681</td>\n","      <td>0.430184</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>None</th>\n","      <td>752.0</td>\n","      <td>0.289894</td>\n","      <td>0.454015</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>Other</th>\n","      <td>752.0</td>\n","      <td>0.007979</td>\n","      <td>0.089026</td>\n","      <td>0.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.0000</td>\n","      <td>1.00</td>\n","    </tr>\n","    <tr>\n","      <th>cgpa_clean</th>\n","      <td>752.0</td>\n","      <td>3.764628</td>\n","      <td>0.446955</td>\n","      <td>2.3</td>\n","      <td>3.46</td>\n","      <td>3.78</td>\n","      <td>4.0825</td>\n","      <td>4.78</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea136a88-4881-46ae-9b25-57701871064d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      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Describing the dataset\n","student.describe().T"]},{"cell_type":"code","execution_count":142,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1723200581958,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"JyYypAMjcvtB","outputId":"bc2e7605-18e4-4929-bfbf-ece0a9d2b8dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 752 entries, 0 to 751\n","Data columns (total 38 columns):\n"," #   Column                     Non-Null Count  Dtype  \n","---  ------                     --------------  -----  \n"," 0   id                         752 non-null    int64  \n"," 1   gender                     752 non-null    object \n"," 2   age_join_uni               752 non-null    int64  \n"," 3   ethnic_region_origin       752 non-null    object \n"," 4   region_residence           752 non-null    object \n"," 5   religion                   752 non-null    object \n"," 6   marital_status             752 non-null    object \n"," 7   any_children               752 non-null    object \n"," 8   year_alevel                746 non-null    float64\n"," 9   region_alevel_school       750 non-null    object \n"," 10  alevel_subject_category    752 non-null    object \n"," 11  alevel_points              745 non-null    float64\n"," 12  leadership_alevel          752 non-null    object \n"," 13  mode_join_univ             752 non-null    object \n"," 14  college                    752 non-null    object \n"," 15  program_code_clean         752 non-null    object \n"," 16  program_duration           752 non-null    int64  \n"," 17  class_size                 752 non-null    object \n"," 18  sponsorship                752 non-null    object \n"," 19  year_entry_uni             752 non-null    int64  \n"," 20  did_grad_on_time           736 non-null    object \n"," 21  was_resident               752 non-null    object \n"," 22  was_employed               752 non-null    object \n"," 23  class_attendence           752 non-null    object \n"," 24  extra_curricular           752 non-null    object \n"," 25  sports                     752 non-null    int64  \n"," 26  leadership                 752 non-null    int64  \n"," 27  volunteering               752 non-null    int64  \n"," 28  clubs                      752 non-null    int64  \n"," 29  ministry_religious         752 non-null    int64  \n"," 30  None                       752 non-null    int64  \n"," 31  Other                      752 non-null    int64  \n"," 32  parent_alive               752 non-null    object \n"," 33  parents_marital_status     752 non-null    object \n"," 34  parents_employment_status  752 non-null    object \n"," 35  other_children_schooling   742 non-null    object \n"," 36  parents_academics          739 non-null    object \n"," 37  cgpa_clean                 752 non-null    float64\n","dtypes: float64(3), int64(11), object(24)\n","memory usage: 223.4+ KB\n"]}],"source":["# Checking for presence of null values and data types of each column to gain a quick overview of the dataset's structure and the quality of the data.\n","student.info()"]},{"cell_type":"code","execution_count":143,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":99},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1723200581958,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"HiH8rfuAc1Qr","outputId":"eefa0044-c67a-4fc0-ff1b-fe89456a7b04"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [id, gender, age_join_uni, ethnic_region_origin, region_residence, religion, marital_status, any_children, year_alevel, region_alevel_school, alevel_subject_category, alevel_points, leadership_alevel, mode_join_univ, college, program_code_clean, program_duration, class_size, sponsorship, year_entry_uni, did_grad_on_time, was_resident, was_employed, class_attendence, extra_curricular, sports, leadership, volunteering, clubs, ministry_religious, None, Other, parent_alive, parents_marital_status, parents_employment_status, other_children_schooling, parents_academics, cgpa_clean]\n","Index: []\n","\n","[0 rows x 38 columns]"],"text/html":["\n","  <div id=\"df-8b96cf77-ac49-4fde-ad9f-2a405555bf13\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>gender</th>\n","      <th>age_join_uni</th>\n","      <th>ethnic_region_origin</th>\n","      <th>region_residence</th>\n","      <th>religion</th>\n","      <th>marital_status</th>\n","      <th>any_children</th>\n","      <th>year_alevel</th>\n","      <th>region_alevel_school</th>\n","      <th>...</th>\n","      <th>clubs</th>\n","      <th>ministry_religious</th>\n","      <th>None</th>\n","      <th>Other</th>\n","      <th>parent_alive</th>\n","      <th>parents_marital_status</th>\n","      <th>parents_employment_status</th>\n","      <th>other_children_schooling</th>\n","      <th>parents_academics</th>\n","      <th>cgpa_clean</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","<p>0 rows × 38 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b96cf77-ac49-4fde-ad9f-2a405555bf13')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" 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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{},"execution_count":143}],"source":["# Checking for duplicates\n","student[student.duplicated()]"]},{"cell_type":"code","execution_count":144,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5873,"status":"ok","timestamp":1723200587824,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"UvWJqB9Oc6xV","outputId":"22c184d9-a195-43d0-ecdd-f5abad787f27"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 2000x2000 with 16 Axes>"],"image/png":"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Visualizing the distribution of data in the dataset and identify any potential outliers or skewness in the data.\n","student.hist(figsize=(20,20));\n","# Adding a title for the plot\n","plt.suptitle(\"Visualizing the distribution of data in the dataset\", fontsize = 20);"]},{"cell_type":"code","execution_count":145,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1723200587824,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"N3q6iVk5c-5x","outputId":"bc4fc421-1e70-4468-81b0-cd2b0a332432"},"outputs":[{"output_type":"stream","name":"stdout","text":["id values \n","\n","id\n","1      1\n","506    1\n","497    1\n","498    1\n","499    1\n","      ..\n","253    1\n","254    1\n","255    1\n","256    1\n","752    1\n","Name: count, Length: 752, dtype: int64\n","gender values \n","\n","gender\n","Male      411\n","Female    341\n","Name: count, dtype: int64\n","age_join_uni values \n","\n","age_join_uni\n","19    279\n","20    189\n","21     82\n","18     73\n","22     35\n","23     25\n","30     20\n","24     12\n","25     10\n","29      8\n","27      7\n","26      4\n","28      3\n","17      1\n","33      1\n","16      1\n","15      1\n","37      1\n","Name: count, dtype: int64\n","ethnic_region_origin values \n","\n","ethnic_region_origin\n","Central Uganda     260\n","Western Uganda     217\n","Eastern Uganda     166\n","Northern Uganda     75\n","Southern Uganda     12\n","South Sudan          5\n","Rwanda               3\n","West Nile            2\n","South Sudan          2\n","Kenya                2\n","West Nile            2\n","Eastern Uganda       1\n","central Uganda       1\n","Tanzanian            1\n","Somalia              1\n","North Eastern        1\n","rwanda               1\n","Name: count, dtype: int64\n","region_residence values \n","\n","region_residence\n","Central Uganda     699\n","Eastern Uganda      26\n","Western Uganda      11\n","Northern Uganda     10\n","central Uganda       3\n","Southern Uganda      2\n","Tanzania             1\n","Name: count, dtype: int64\n","religion values \n","\n","religion\n","Protestant               310\n","Catholic                 250\n","Other                    108\n","Muslim                    61\n","Seventh Day Adventist     17\n","Atheist                    4\n","catholic                   2\n","Name: count, dtype: int64\n","marital_status values \n","\n","marital_status\n","Single        704\n","Married        37\n","Cohabiting     10\n","Separated       1\n","Name: count, dtype: int64\n","any_children values \n","\n","any_children\n","No     698\n","Yes     54\n","Name: count, dtype: int64\n","year_alevel values \n","\n","year_alevel\n","2018.0    260\n","2017.0    188\n","2015.0     48\n","2014.0     42\n","2013.0     36\n","2016.0     35\n","2012.0     27\n","2011.0     16\n","2010.0     11\n","2019.0     10\n","2002.0     10\n","2003.0      9\n","2006.0      7\n","2008.0      7\n","2005.0      7\n","2009.0      6\n","2004.0      5\n","1998.0      4\n","1999.0      4\n","1996.0      2\n","2007.0      2\n","2020.0      2\n","2000.0      2\n","1992.0      1\n","2001.0      1\n","1997.0      1\n","1985.0      1\n","1995.0      1\n","1988.0      1\n","Name: count, dtype: int64\n","region_alevel_school values \n","\n","region_alevel_school\n","Central Uganda     534\n","Western Uganda     105\n","Eastern Uganda      77\n","Northern Uganda     21\n","Southern Uganda      8\n","Rwanda               3\n","Kenya                1\n","Somalia              1\n","Name: count, dtype: int64\n","alevel_subject_category values \n","\n","alevel_subject_category\n","SCIENCES    389\n","ARTS        363\n","Name: count, dtype: int64\n","alevel_points values \n","\n","alevel_points\n","14.0    84\n","15.0    82\n","13.0    80\n","12.0    76\n","16.0    73\n","17.0    61\n","11.0    56\n","18.0    47\n","19.0    39\n","10.0    31\n","9.0     27\n","20.0    23\n","8.0     20\n","7.0     16\n","22.0     7\n","6.0      7\n","23.0     4\n","21.0     4\n","25.0     3\n","5.0      2\n","24.0     1\n","4.0      1\n","3.0      1\n","Name: count, dtype: int64\n","leadership_alevel values \n","\n","leadership_alevel\n","Yes    466\n","No     286\n","Name: count, dtype: int64\n","mode_join_univ values \n","\n","mode_join_univ\n","From Senior Six    650\n","Degree              52\n","Diploma             39\n","Mature Entry        11\n","Name: count, dtype: int64\n","college values \n","\n","college\n","College C    290\n","College A    122\n","College B    104\n","College G     60\n","College D     43\n","College E     42\n","College H     36\n","College F     29\n","College I     26\n","Name: count, dtype: int64\n","program_code_clean values \n","\n","program_code_clean\n","Program 56    61\n","Program 4     51\n","Program 18    50\n","Program 1     48\n","Program 3     48\n","              ..\n","Program 45     1\n","Program 38     1\n","Program 30     1\n","Program 17     1\n","Program 86     1\n","Name: count, Length: 86, dtype: int64\n","program_duration values \n","\n","program_duration\n","3    643\n","4     91\n","5     10\n","2      8\n","Name: count, dtype: int64\n","class_size values \n","\n","class_size\n","Between 100 and 250 Students    326\n","Less than 100 Students          269\n","Between 250 and 750 Students    131\n","Over 750 Students                23\n","Over 1000 Students                3\n","Name: count, dtype: int64\n","sponsorship values \n","\n","sponsorship\n","Private        510\n","Government     188\n","Scholarship     54\n","Name: count, dtype: int64\n","year_entry_uni values \n","\n","year_entry_uni\n","2019    308\n","2018    215\n","2016     50\n","2015     46\n","2014     30\n","2013     20\n","2017     11\n","2012     10\n","2020      9\n","2004      8\n","2003      7\n","2006      6\n","2007      6\n","2009      5\n","2000      3\n","2002      2\n","2021      2\n","2010      2\n","2008      2\n","1999      2\n","2005      2\n","1998      1\n","1995      1\n","2001      1\n","1984      1\n","2011      1\n","1997      1\n","Name: count, dtype: int64\n","did_grad_on_time values \n","\n","did_grad_on_time\n","Yes    648\n","No      88\n","Name: count, dtype: int64\n","was_resident values \n","\n","was_resident\n","No     612\n","Yes    140\n","Name: count, dtype: int64\n","was_employed values \n","\n","was_employed\n","No     608\n","Yes    144\n","Name: count, dtype: int64\n","class_attendence values \n","\n","class_attendence\n","Regularly Attended    600\n","Fairly Attended       138\n","Rarely Attended        13\n","Never Attended          1\n","Name: count, dtype: int64\n","extra_curricular values \n","\n","extra_curricular\n","Yes    533\n","No     219\n","Name: count, dtype: int64\n","sports values \n","\n","sports\n","0    524\n","1    228\n","Name: count, dtype: int64\n","leadership values \n","\n","leadership\n","0    568\n","1    184\n","Name: count, dtype: int64\n","volunteering values \n","\n","volunteering\n","0    554\n","1    198\n","Name: count, dtype: int64\n","clubs values \n","\n","clubs\n","0    625\n","1    127\n","Name: count, dtype: int64\n","ministry_religious values \n","\n","ministry_religious\n","0    568\n","1    184\n","Name: count, dtype: int64\n","None values \n","\n","None\n","0    534\n","1    218\n","Name: count, dtype: int64\n","Other values \n","\n","Other\n","0    746\n","1      6\n","Name: count, dtype: int64\n","parent_alive values \n","\n","parent_alive\n","Yes         704\n","No (RIP)     48\n","Name: count, dtype: int64\n","parents_marital_status values \n","\n","parents_marital_status\n","Married       450\n","Separated     101\n","Widowed        75\n","Single         56\n","Deceased       32\n","Divorced       20\n","Cohabiting     18\n","Name: count, dtype: int64\n","parents_employment_status values \n","\n","parents_employment_status\n","Employed      495\n","Unemployed    257\n","Name: count, dtype: int64\n","other_children_schooling values \n","\n","other_children_schooling\n","Yes    428\n","No     314\n","Name: count, dtype: int64\n","parents_academics values \n","\n","parents_academics\n","Not Educated    173\n","Bachelors       155\n","High School     146\n","Diploma         116\n","Masters         106\n","PhD              24\n","Certificate      11\n","Primary           8\n","Name: count, dtype: int64\n","cgpa_clean values \n","\n","cgpa_clean\n","4.00    13\n","3.40    12\n","3.90    12\n","3.82    11\n","3.88    11\n","        ..\n","2.58     1\n","4.44     1\n","2.88     1\n","4.14     1\n","2.87     1\n","Name: count, Length: 188, dtype: int64\n"]}],"source":["### Investigate Data\n","col_list = student.columns\n","\n","for i in col_list:\n","  print(i+' values \\n')\n","  print(student[i].value_counts())"]},{"cell_type":"code","execution_count":146,"metadata":{"id":"xeGIVPE0dzQO","executionInfo":{"status":"ok","timestamp":1723200587825,"user_tz":-180,"elapsed":7,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["# We need to import the ProfileReport so be able to generate Profiling Report\n","from ydata_profiling import ProfileReport"]},{"cell_type":"code","execution_count":147,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":367,"referenced_widgets":["7910f06441104572b2e8a0658050a9ab","8d97fade99644c929471299c41136e4d","14a986b08d024797a2af7d1524a57818","7eb21f8a09d54561ab3ab8ed3916ec72","7ed4fc1f750244b392e6845ef59bcd3c","087c351b2d9f48909b2f821ab786ea10","e10741dacaf34982911789927e011095","13168d9e5fe04c51b4fd071b9aa6025d","08dd26bc0c5647acba1ac5f81f8b86a6","f1be488b697f45b9a5b1cb0d07680359","396564754183459aa5daf4cab242465e","4569ae3cbbc240dbb38595b2d72466b5","aa3b247d779d410d9d903f175f11b096","b57c08dcc48644508e76fa8a1dab36a4","99a6b4ff9f54492e88c7d0e31c17be97","95cc496d368c45279d1691da71ccc5ce","f888028e2b444fbcbb0602dfb489bd92","3cb5e0f645534603b574f1ce60dd1740","16c78bca54714808ab2a501f8ae959de","aa7ff445e09c45e68406ff85e32d5b24","64fb99fc49b04972a0af18433df59921","59c24bf10fc9414caa522370c31044d6","8bb6fd652f4040228a1b076ccbad6ebc","0825d9d1573f4b2694c587408d437d68","ecdfbf5dce0e4755b402026350a39fd7","0c33d13ba2b64b12a766a71792f4d4ea","7ba75990df604bada837f2548e072ce6","2125dea20fc0444cac1ebba14ba72848","7d5e95a2223c47feab465bf9f5525196","d0a98e07d2ae4c0bb9cf7492c1f57ea1","fe9d65942fd54a7f92dadb3246dbb98a","602765cbe73049d890b9f7a485bcb2d2","7d8345454db742278c60d360fdd45f6e","deeebb9e06854827afdec19c38333e90","4bc75c07e5ec4095aa42f574d6a5bda8","f870c3d14e774378b4bea5a2f267af3e","b245aa23a6224930a43f851e5306b26a","acde7090d02946c681aea527b1924f83","de12723a2a8647378408e7567b2f7d83","4427f196185e4365ba0902c8ba4618a4","39a1bed79bdb4a239295d56fafa2c1de","dcd4eebb28984d5d9139f43f928c5ee4","0de8f5d36189449f836b482052e25739","571fcd170f9945aaa26e6f8398d8f8d5"]},"id":"dJdRYgCseAUg","outputId":"faae882d-8cdc-4e6a-ba1f-ff1f6a2413d8","executionInfo":{"status":"ok","timestamp":1723200636762,"user_tz":-180,"elapsed":48943,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ydata_profiling/profile_report.py:363: UserWarning: Try running command: 'pip install --upgrade Pillow' to avoid ValueError\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7910f06441104572b2e8a0658050a9ab"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ydata_profiling/model/correlations.py:66: UserWarning: There was an attempt to calculate the auto correlation, but this failed.\n","To hide this warning, disable the calculation\n","(using `df.profile_report(correlations={\"auto\": {\"calculate\": False}})`\n","If this is problematic for your use case, please report this as an issue:\n","https://github.com/ydataai/ydata-profiling/issues\n","(include the error message: 'Function <code object pandas_auto_compute at 0x78b082493c00, file \"/usr/local/lib/python3.10/dist-packages/ydata_profiling/model/pandas/correlations_pandas.py\", line 167>')\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4569ae3cbbc240dbb38595b2d72466b5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8bb6fd652f4040228a1b076ccbad6ebc"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ydata_profiling/profile_report.py:384: UserWarning: Extension .pdf not supported. For now we assume .html was intended. To remove this warning, please use .html or .json.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"deeebb9e06854827afdec19c38333e90"}},"metadata":{}}],"source":["# Generate profile Report\n","profile = ProfileReport(student, title='Student Data Profiling Report', explorative=True)\n","profile.to_file('Student Data Profiling Report-new.pdf')"]},{"cell_type":"code","execution_count":148,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":822,"output_embedded_package_id":"189U5N1Orp1reyd9r4T0-AViacCRamW_P"},"id":"sB31AiimeEMr","outputId":"3602720f-e03e-4e14-a20b-8104c4907186","executionInfo":{"status":"ok","timestamp":1723200639508,"user_tz":-180,"elapsed":2758,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}],"source":["# Displaying the pandas profiling report within the Jupyter Notebook\n","profile.to_notebook_iframe()"]},{"cell_type":"code","execution_count":149,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"A81dOJ7BffJG","outputId":"649e72b9-89d5-48d7-836d-e67b3655b0d8","executionInfo":{"status":"ok","timestamp":1723200639508,"user_tz":-180,"elapsed":21,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["id                            0\n","gender                        0\n","age_join_uni                  0\n","ethnic_region_origin          0\n","region_residence              0\n","religion                      0\n","marital_status                0\n","any_children                  0\n","year_alevel                   6\n","region_alevel_school          2\n","alevel_subject_category       0\n","alevel_points                 7\n","leadership_alevel             0\n","mode_join_univ                0\n","college                       0\n","program_code_clean            0\n","program_duration              0\n","class_size                    0\n","sponsorship                   0\n","year_entry_uni                0\n","did_grad_on_time             16\n","was_resident                  0\n","was_employed                  0\n","class_attendence              0\n","extra_curricular              0\n","sports                        0\n","leadership                    0\n","volunteering                  0\n","clubs                         0\n","ministry_religious            0\n","None                          0\n","Other                         0\n","parent_alive                  0\n","parents_marital_status        0\n","parents_employment_status     0\n","other_children_schooling     10\n","parents_academics            13\n","cgpa_clean                    0\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>id</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>gender</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>age_join_uni</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>ethnic_region_origin</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>region_residence</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>religion</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>marital_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>any_children</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>year_alevel</th>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>region_alevel_school</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_subject_category</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_points</th>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>leadership_alevel</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>mode_join_univ</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>college</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>program_code_clean</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>program_duration</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>class_size</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>sponsorship</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>year_entry_uni</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>did_grad_on_time</th>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>was_resident</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>was_employed</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>class_attendence</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>extra_curricular</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>sports</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>leadership</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>volunteering</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>clubs</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>ministry_religious</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>None</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Other</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parent_alive</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_marital_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_employment_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>other_children_schooling</th>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>parents_academics</th>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>cgpa_clean</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":149}],"source":["### Check for missing values\n","student.isnull().sum()"]},{"cell_type":"code","execution_count":150,"metadata":{"id":"wDqX1BUOf2_Y","executionInfo":{"status":"ok","timestamp":1723200639508,"user_tz":-180,"elapsed":20,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["# Make a copy of the data before performing imputations\n","student_clean = student.copy()"]},{"cell_type":"code","execution_count":151,"metadata":{"id":"DLQU-wsEgp-J","executionInfo":{"status":"ok","timestamp":1723200639509,"user_tz":-180,"elapsed":21,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["## Function to impute median and Random this will be compared by graphing to chose the best\n","def impute_nan(df,variable,median):\n","    df[variable+\"_median\"]=df[variable].fillna(median)\n","    df[variable+\"_random\"]=df[variable]\n","    random_sample=df[variable].dropna().sample(df[variable].isnull().sum(), random_state=0)\n","    ##pandas need to have same index in order to merge the dataset\n","    random_sample.index=df[df[variable].isnull()].index\n","    df.loc[df[variable].isnull(),variable+'_random']=random_sample"]},{"cell_type":"code","execution_count":152,"metadata":{"id":"lgmVyX-qg4Vu","executionInfo":{"status":"ok","timestamp":1723200639509,"user_tz":-180,"elapsed":21,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["median=student_clean.alevel_points.median()"]},{"cell_type":"code","execution_count":153,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b59pHcBDg7_l","outputId":"373288cc-0e1a-47aa-b90c-a317825367e8","executionInfo":{"status":"ok","timestamp":1723200639509,"user_tz":-180,"elapsed":20,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["14.0"]},"metadata":{},"execution_count":153}],"source":["median"]},{"cell_type":"code","execution_count":154,"metadata":{"id":"7WDuhYG5g-JX","executionInfo":{"status":"ok","timestamp":1723200639509,"user_tz":-180,"elapsed":19,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["#Impute the  Values\n","impute_nan(student_clean,\"alevel_points\",median)"]},{"cell_type":"code","execution_count":155,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"E55l5BFjhAtp","outputId":"92b9b517-b6ea-44c6-c981-43ba021c9a26","executionInfo":{"status":"ok","timestamp":1723200639509,"user_tz":-180,"elapsed":18,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x78b0807d1c60>"]},"metadata":{},"execution_count":155},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plot the graph comparing the alevel_points, alevel_points_median and alevel_points_random\n","fig = plt.figure()\n","ax = fig.add_subplot(111)\n","student_clean['alevel_points'].plot(kind='kde', ax=ax)\n","student_clean.alevel_points_median.plot(kind='kde', ax=ax, color='red')\n","student_clean.alevel_points_random.plot(kind='kde', ax=ax, color='green')\n","lines, labels = ax.get_legend_handles_labels()\n","ax.legend(lines, labels, loc='best')"]},{"cell_type":"code","execution_count":156,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CR0c1prhhSBt","outputId":"fa865a1a-2629-463c-c9f4-a7eab199c421","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":24,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["7"]},"metadata":{},"execution_count":156}],"source":["## Advantages of Random Imputation\n","#### Easy To implement\n","#### here is less distortion in variance\n","\n","## Disadvantage of Random Imputation\n","#### Every situation randomness wont work\n","\n","student_clean['alevel_points'].isnull().sum()"]},{"cell_type":"code","execution_count":157,"metadata":{"id":"9Uo5ccLlhuyG","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":24,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["# Imputations\n","student_clean['alevel_points'].fillna(student_clean.alevel_points_random, inplace=True)"]},{"cell_type":"code","execution_count":158,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XI_aW3-Vhyfb","outputId":"d9a961be-8ddc-4ad0-fd21-e52a2f9b9c09","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":22,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{},"execution_count":158}],"source":["# Verification of the imputations\n","student_clean['alevel_points'].isnull().sum()"]},{"cell_type":"code","source":["#Drop the created columns of alevel_points_median and alevel_points_random\n","student_clean.drop(['alevel_points_median','alevel_points_random'], axis=1, inplace=True)"],"metadata":{"id":"LxXqJS0erPb6","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":22,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"execution_count":159,"outputs":[]},{"cell_type":"code","execution_count":160,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"JlYJ3LGeh0XZ","outputId":"689f39a5-6d5c-4615-c1ce-d06b29b4f282","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":22,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["id                            0\n","gender                        0\n","age_join_uni                  0\n","ethnic_region_origin          0\n","region_residence              0\n","religion                      0\n","marital_status                0\n","any_children                  0\n","year_alevel                   6\n","region_alevel_school          2\n","alevel_subject_category       0\n","alevel_points                 0\n","leadership_alevel             0\n","mode_join_univ                0\n","college                       0\n","program_code_clean            0\n","program_duration              0\n","class_size                    0\n","sponsorship                   0\n","year_entry_uni                0\n","did_grad_on_time             16\n","was_resident                  0\n","was_employed                  0\n","class_attendence              0\n","extra_curricular              0\n","sports                        0\n","leadership                    0\n","volunteering                  0\n","clubs                         0\n","ministry_religious            0\n","None                          0\n","Other                         0\n","parent_alive                  0\n","parents_marital_status        0\n","parents_employment_status     0\n","other_children_schooling     10\n","parents_academics            13\n","cgpa_clean                    0\n","dtype: int64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>id</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>gender</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>age_join_uni</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>ethnic_region_origin</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>region_residence</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>religion</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>marital_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>any_children</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>year_alevel</th>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>region_alevel_school</th>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_subject_category</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_points</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>leadership_alevel</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>mode_join_univ</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>college</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>program_code_clean</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>program_duration</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>class_size</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>sponsorship</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>year_entry_uni</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>did_grad_on_time</th>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>was_resident</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>was_employed</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>class_attendence</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>extra_curricular</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>sports</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>leadership</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>volunteering</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>clubs</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>ministry_religious</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>None</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>Other</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parent_alive</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_marital_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_employment_status</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>other_children_schooling</th>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>parents_academics</th>\n","      <td>13</td>\n","    </tr>\n","    <tr>\n","      <th>cgpa_clean</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":160}],"source":["student_clean.isnull().sum()"]},{"cell_type":"code","execution_count":161,"metadata":{"id":"KC3gR-LaiTUG","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":22,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["### Replacing the other Features with the Most Frequent since the Percentage are very low\n","### Function to impute\n","\n","def impute_nan(df,variable):\n","    most_frequent_category=df[variable].mode()[0]\n","    df[variable].fillna(most_frequent_category,inplace=True)"]},{"cell_type":"code","execution_count":162,"metadata":{"id":"wXViWcN2ieLr","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":22,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["for feature in ['year_alevel','region_alevel_school','did_grad_on_time','other_children_schooling','parents_academics']:\n","    impute_nan(student_clean,feature)"]},{"cell_type":"code","execution_count":163,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"IB_pvaHyiiQT","outputId":"ae9dc8a7-5c68-4ae2-c1d1-a29420ff7885","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":21,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["id                           0.0\n","gender                       0.0\n","age_join_uni                 0.0\n","ethnic_region_origin         0.0\n","region_residence             0.0\n","religion                     0.0\n","marital_status               0.0\n","any_children                 0.0\n","year_alevel                  0.0\n","region_alevel_school         0.0\n","alevel_subject_category      0.0\n","alevel_points                0.0\n","leadership_alevel            0.0\n","mode_join_univ               0.0\n","college                      0.0\n","program_code_clean           0.0\n","program_duration             0.0\n","class_size                   0.0\n","sponsorship                  0.0\n","year_entry_uni               0.0\n","did_grad_on_time             0.0\n","was_resident                 0.0\n","was_employed                 0.0\n","class_attendence             0.0\n","extra_curricular             0.0\n","sports                       0.0\n","leadership                   0.0\n","volunteering                 0.0\n","clubs                        0.0\n","ministry_religious           0.0\n","None                         0.0\n","Other                        0.0\n","parent_alive                 0.0\n","parents_marital_status       0.0\n","parents_employment_status    0.0\n","other_children_schooling     0.0\n","parents_academics            0.0\n","cgpa_clean                   0.0\n","dtype: float64"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>id</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>gender</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>age_join_uni</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>ethnic_region_origin</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>region_residence</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>religion</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>marital_status</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>any_children</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>year_alevel</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>region_alevel_school</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_subject_category</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>alevel_points</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>leadership_alevel</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>mode_join_univ</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>college</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>program_code_clean</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>program_duration</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>class_size</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>sponsorship</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>year_entry_uni</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>did_grad_on_time</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>was_resident</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>was_employed</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>class_attendence</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>extra_curricular</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>sports</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>leadership</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>volunteering</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>clubs</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>ministry_religious</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>None</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>Other</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>parent_alive</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_marital_status</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_employment_status</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>other_children_schooling</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>parents_academics</th>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>cgpa_clean</th>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div><br><label><b>dtype:</b> float64</label>"]},"metadata":{},"execution_count":163}],"source":["student_clean.isnull().mean()"]},{"cell_type":"code","execution_count":164,"metadata":{"id":"W59WIIc1cRUA","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":21,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"outputs":[],"source":["# Make a copy of the DataFrame\n","student_clean_new = student_clean.copy()"]},{"cell_type":"code","execution_count":165,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1723200639517,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"},"user_tz":-180},"id":"cp4Qmu3ahpxK","outputId":"2a4ae336-3cd2-446a-9f7e-3fbd0aed5cc7"},"outputs":[{"output_type":"stream","name":"stdout","text":["The copied DataFrame has been saved to students_dataset_article_imputed_missing_data.csv\n"]}],"source":["#Save the copied DataFrame to a file (e.g., CSV)\n","file_name = 'students_dataset_article_imputed_missing_data.csv'\n","student_clean_new.to_csv(file_name, index=False)  # You can use other formats like Excel or JSON by changing the method\n","\n","# Check if the file is saved successfully\n","import os\n","if os.path.exists(file_name):\n","    print(f\"The copied DataFrame has been saved to {file_name}\")\n","else:\n","    print(\"Failed to save the DataFrame.\")\n"]},{"cell_type":"code","source":[],"metadata":{"id":"Fi_m3zNHfjsC","executionInfo":{"status":"ok","timestamp":1723200639517,"user_tz":-180,"elapsed":19,"user":{"displayName":"Tony Oluka","userId":"13501629377579371104"}}},"execution_count":165,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"1rBWcmxegN0jdnKGAm7q-0TQKsx5sddB8","timestamp":1720532337598},{"file_id":"1cEdIFaqKMzzZeVjSirRIc6PTMLbs_uQW","timestamp":1718958068056}],"authorship_tag":"ABX9TyPeBXzSY6feK1KaGBEKcKgn"},"kernelspec":{"display_name":"Python 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